2.3 TAI enrichment
Are some PS enriched?
Using myTAI::pStrata ->
ggplot2::geom_col or ggplot2::geom_area
doesn’t lead to interesting results. The way to do it is probably with a
heatmap. myTAI::pTAI also doesn’t look so nice.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
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## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(myTAI)
# myTAI v1.0.1.9000 2023-07-12 Github (drostlab/myTAI@46eb927)Playing around with PhyloExpressionSetExample
Based on this, I can intuit which type of analysis may be appropriate.
data(PhyloExpressionSetExample)
heatmap(myTAI::pStrata(PhyloExpressionSetExample))
pStrata_data <-
tibble::as_tibble(myTAI::pStrata(PhyloExpressionSetExample), rownames = "PS") %>%
tidyr::pivot_longer(!PS, names_to = "Stage", values_to = "pStrata")
pStrata_data$Stage <-
pStrata_data$Stage <- base::factor(pStrata_data$Stage, unique(pStrata_data$Stage))
pStrata_data %>%
ggplot2::ggplot(aes(x = Stage, y = PS, fill = pStrata)) +
ggplot2::geom_tile()
#
# pStrata_data %>%
# ggplot2::ggplot(aes(x = Stage, y = pStrata, fill = PS)) +
# ggplot2::geom_col()
#
# pStrata_data2 <-
# pStrata_data %>%
# dplyr::mutate(
# Stage_number = dplyr::case_when(
# Stage == "Zygote" ~ 1,
# Stage == "Quadrant" ~ 2,
# Stage == "Globular" ~ 3,
# Stage == "Heart" ~ 4,
# Stage == "Torpedo" ~ 5,
# Stage == "Bent" ~ 6,
# Stage == "Mature" ~ 7
# )
# ) %>%
# dplyr::group_by(Stage) %>%
# dplyr::mutate(
# prop = pStrata/sum(pStrata)
# )
#
# pStrata_data2 %>%
# ggplot2::ggplot(aes(x = Stage_number, y = prop, fill = PS)) +
# ggplot2::geom_area(alpha=0.6 , size=1, colour="black")# # Works but uninteresting.
# pTAI_data <-
# tibble::as_tibble(myTAI::pTAI(PhyloExpressionSetExample), rownames = "PS") %>%
# tidyr::pivot_longer(!PS, names_to = "Stage", values_to = "pTAI_accumulated")
#
# pTAI_data$Stage <-
# pTAI_data$Stage <- base::factor(pTAI_data$Stage, unique(pTAI_data$Stage))
#
# pTAI_data %>%
# ggplot2::ggplot(aes(x = Stage, y = pTAI_accumulated, group = PS, colour = PS)) +
# ggplot2::geom_line()PlotCategoryExpr(ExpressionSet = myTAI::tf(PhyloExpressionSetExample, FUN = log2, pseudocount = 1),
legendName = "PS",
test.stat = TRUE,
type = "category-centered",
distr.type = "boxplot",
log.expr = FALSE)## Zygote Quadrant Globular Heart Torpedo Bent Mature
## category-centered "***" "***" "***" "***" "***" "***" "***"
PlotCategoryExpr(ExpressionSet = myTAI::tf(PhyloExpressionSetExample, FUN = log2, pseudocount = 1),
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10
## stage-centered "***" "***" "n.s." "*" "n.s." "*" "n.s." "n.s." "n.s." "***"
## PS11 PS12
## stage-centered "n.s." "***"
Fucus analysis
Here, I focus on the stages that generates the Fucus embryo,
i.e. stages not gamates nor
matSP.
Fd_PES <-
readr::read_csv(file = "data/Fd_PES.csv") %>%
dplyr::select(!c("gamete","matSP"))## Rows: 7907 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (9): PS, gamete, E1, E2, E3, E4, E5, E6, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES <-
readr::read_csv(file = "data/Fs_PES.csv") %>%
dplyr::select(!c("gamete","matSP"))## Rows: 8291 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): PS, gamete, 24H, 48H, 1w, 3w, 4w, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.sqrt-tranformed
Fd_PES.sqrt <-
readr::read_csv(file = "data/Fd_PES.sqrt.csv") %>%
dplyr::select(!c("gamete","matSP"))## Rows: 7907 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (9): PS, gamete, E1, E2, E3, E4, E5, E6, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES.sqrt <-
readr::read_csv(file = "data/Fs_PES.sqrt.csv") %>%
dplyr::select(!c("gamete","matSP"))## Rows: 8291 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): PS, gamete, 24H, 48H, 1w, 3w, 4w, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.log2-tranformed
Fd_PES.log2 <-
readr::read_csv(file = "data/Fd_PES.log2.csv") %>%
dplyr::select(!c("gamete","matSP"))## Rows: 7907 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (9): PS, gamete, E1, E2, E3, E4, E5, E6, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES.log2 <-
readr::read_csv(file = "data/Fs_PES.log2.csv") %>%
dplyr::select(!c("gamete","matSP"))## Rows: 8291 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): PS, gamete, 24H, 48H, 1w, 3w, 4w, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# different sexes
Fd_PES_sex.log2 <-
readr::read_csv(file = "data/Fd_PES_M.log2.csv") %>%
dplyr::rename(gamete_M = V1)## Rows: 7907 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): PS, V1
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_sex.log2 <-
readr::read_csv(file = "data/Fd_PES_F.log2.csv") %>%
dplyr::rename(gamete_F = V1) %>%
left_join(Fd_PES_sex.log2)## Rows: 7907 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): PS, V1
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Joining with `by = join_by(PS, GeneID)`Fs_PES_sex.log2 <-
readr::read_csv(file = "data/Fs_PES_M.log2.csv") %>%
dplyr::rename_with(
~ paste0(.x, "_M"),
dplyr::starts_with(c("gamete", "mat")) &! dplyr::ends_with("_F"))## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): PS, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_sex.log2 <-
readr::read_csv(file = "data/Fs_PES_F.log2.csv") %>%
dplyr::rename_with(
~ paste0(.x, "_F"),
dplyr::starts_with(c("gamete", "mat")) &! dplyr::ends_with("_M")) %>%
dplyr::left_join(Fs_PES_sex.log2)## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): PS, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Joining with `by = join_by(PS, GeneID)`Fs_PES_sex_mat.log2 <-
Fs_PES_sex.log2 %>%
dplyr::select(!dplyr::starts_with("gamete"))
Fs_PES_sex_gam.log2 <-
Fs_PES_sex.log2 %>%
dplyr::select(!dplyr::starts_with("mat"))
# different tissues
Fd_PES_matSP.log2 <-
readr::read_csv(file = "data/Fd_PES_matSP.log2.csv")## Rows: 7907 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (5): PS, holdfast, reptip, stipe, vegtip
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_M_matSP.log2 <-
readr::read_csv(file = "data/Fs_PES_M_matSP.log2.csv")## Rows: 8291 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (5): PS, holdfast, reptip, stipe, vegtip
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_F_matSP.log2 <-
readr::read_csv(file = "data/Fs_PES_F_matSP.log2.csv")## Rows: 8291 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (5): PS, holdfast, reptip, stipe, vegtip
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_matSP_holdfast.log2 <-
Fs_PES_M_matSP.log2 %>%
dplyr::select(1:2 | dplyr::starts_with("holdfast")) %>%
dplyr::rename_with(
~ paste0(.x, "_M"),
dplyr::starts_with(c("holdfast")) &! dplyr::ends_with("_F"))
Fs_PES_matSP_holdfast.log2 <-
Fs_PES_F_matSP.log2 %>%
dplyr::select(1:2 | dplyr::starts_with("holdfast")) %>%
dplyr::rename_with(
~ paste0(.x, "_F"),
dplyr::starts_with(c("holdfast")) &! dplyr::ends_with("_M")) %>%
dplyr::left_join(Fs_PES_matSP_holdfast.log2)## Joining with `by = join_by(PS, GeneID)`Fs_PES_matSP_reptip.log2 <-
Fs_PES_M_matSP.log2 %>%
dplyr::select(1:2 | dplyr::starts_with("reptip")) %>%
dplyr::rename_with(
~ paste0(.x, "_M"),
dplyr::starts_with(c("reptip")) &! dplyr::ends_with("_F"))
Fs_PES_matSP_reptip.log2 <-
Fs_PES_F_matSP.log2 %>%
dplyr::select(1:2 | dplyr::starts_with("reptip")) %>%
dplyr::rename_with(
~ paste0(.x, "_F"),
dplyr::starts_with(c("reptip")) &! dplyr::ends_with("_M")) %>%
dplyr::left_join(Fs_PES_matSP_reptip.log2)## Joining with `by = join_by(PS, GeneID)`Fs_PES_matSP_stipe.log2 <-
Fs_PES_M_matSP.log2 %>%
dplyr::select(1:2 | dplyr::starts_with("stipe")) %>%
dplyr::rename_with(
~ paste0(.x, "_M"),
dplyr::starts_with(c("stipe")) &! dplyr::ends_with("_F"))
Fs_PES_matSP_stipe.log2 <-
Fs_PES_F_matSP.log2 %>%
dplyr::select(1:2 | dplyr::starts_with("stipe")) %>%
dplyr::rename_with(
~ paste0(.x, "_F"),
dplyr::starts_with(c("stipe")) &! dplyr::ends_with("_M")) %>%
dplyr::left_join(Fs_PES_matSP_stipe.log2)## Joining with `by = join_by(PS, GeneID)`Fs_PES_matSP_vegtip.log2 <-
Fs_PES_M_matSP.log2 %>%
dplyr::select(1:2 | dplyr::starts_with("vegtip")) %>%
dplyr::rename_with(
~ paste0(.x, "_M"),
dplyr::starts_with(c("vegtip")) &! dplyr::ends_with("_F"))
Fs_PES_matSP_vegtip.log2 <-
Fs_PES_F_matSP.log2 %>%
dplyr::select(1:2 | dplyr::starts_with("vegtip")) %>%
dplyr::rename_with(
~ paste0(.x, "_F"),
dplyr::starts_with(c("vegtip")) &! dplyr::ends_with("_M")) %>%
dplyr::left_join(Fs_PES_matSP_vegtip.log2)## Joining with `by = join_by(PS, GeneID)`rank-tranformed
Fd_PES.rank <-
readr::read_csv(file = "data/Fd_PES.rank.csv") %>%
dplyr::select(!c("gamete","matSP"))## Rows: 7907 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (9): PS, gamete, E1, E2, E3, E4, E5, E6, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES.rank <-
readr::read_csv(file = "data/Fs_PES.rank.csv") %>%
dplyr::select(!c("gamete","matSP"))## Rows: 8291 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): PS, gamete, 24H, 48H, 1w, 3w, 4w, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.rlog-tranformed
Fd_PES.rlog <-
readr::read_csv(file = "data/Fd_PES.rlog.csv") %>%
dplyr::select(!c("gamete","matSP"))## Rows: 7907 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (9): PS, gamete, E1, E2, E3, E4, E5, E6, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES.rlog <-
readr::read_csv(file = "data/Fs_PES.rlog.csv") %>%
dplyr::select(!c("gamete","matSP"))## Rows: 8291 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): PS, gamete, 24H, 48H, 1w, 3w, 4w, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.PlotCategoryExpr()
Fucus distichus
# PlotCategoryExpr(
# ExpressionSet = Fd_PES.log2,
# legendName = "PS",
# test.stat = TRUE,
# type = "category-centered",
# distr.type = "boxplot",
# log.expr = FALSE) +
# ggplot2::labs(
# title = "Fucus distichus",
# subtitle = "log2(TPM+1)"
# )
PlotCategoryExpr(
ExpressionSet = Fd_PES.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus distichus development",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "***" "**" "n.s." "n.s." "n.s." "n.s." "***" "***"
PlotCategoryExpr(
ExpressionSet = Fd_PES_sex.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus distichus gamete sexes",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "***" "***" "n.s." "n.s." "*" "***" "***" "***"
PlotCategoryExpr(
ExpressionSet = Fd_PES_matSP.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus distichus matSP tissues",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "***" "***" "n.s." "*" "n.s." "**" "***" "***"
Fucus serratus
# PlotCategoryExpr(
# ExpressionSet = Fs_PES.log2,
# legendName = "PS",
# test.stat = TRUE,
# type = "category-centered",
# distr.type = "boxplot",
# log.expr = FALSE) +
# ggplot2::labs(
# title = "Fucus serratus",
# subtitle = "log2(TPM+1)"
# )
PlotCategoryExpr(
ExpressionSet = Fs_PES.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus serratus development",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "***" "*" "n.s." "n.s." "n.s." "n.s." "*" "***"
PlotCategoryExpr(
ExpressionSet = Fs_PES_sex_gam.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus serratus gamete sexes",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "***" "***" "*" "*" "***" "***" "***" "***"
PlotCategoryExpr(
ExpressionSet = Fs_PES_sex_mat.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus serratus matSP sexes",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "***"
PlotCategoryExpr(
ExpressionSet = Fs_PES_M_matSP.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus serratus matSP (male) tissues",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "***" "***" "**" "***" "**" "***" "***" "***"
PlotCategoryExpr(
ExpressionSet = Fs_PES_F_matSP.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus serratus matSP (female) tissues",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "***" "***" "n.s." "n.s." "n.s." "*" "***" "***"
PlotCategoryExpr(
ExpressionSet = Fs_PES_matSP_vegtip.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus serratus matSP vegtip",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s."
PlotCategoryExpr(
ExpressionSet = Fs_PES_matSP_reptip.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus serratus matSP reptip",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "***" "***" "n.s." "**" "n.s." "**" "***" "***"
PlotCategoryExpr(
ExpressionSet = Fs_PES_matSP_stipe.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus serratus matSP stipe",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "***" "***" "n.s." "n.s." "n.s." "n.s." "***" "***"
PlotCategoryExpr(
ExpressionSet = Fs_PES_matSP_holdfast.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Fucus serratus matSP holdfast",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8
## stage-centered "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "***"
The results are a lot clearer with our RNA-seq data compared to the microarray data of PhyloExpressionSetExample.
Ectocarpus analysis
Here, I focus on the stages that generates the Fucus embryo,
i.e. stages not gamates nor
matSP.
Ec_PES_32m <-
readr::read_csv(file = "data/Ec_PES_32m.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_25f <-
readr::read_csv(file = "data/Ec_PES_25f.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.sqrt-tranformed
Ec_PES_32m.sqrt <-
readr::read_csv(file = "data/Ec_PES_32m.sqrt.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_25f.sqrt <-
readr::read_csv(file = "data/Ec_PES_25f.sqrt.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.log2-tranformed
Ec_PES_32m.log2 <-
readr::read_csv(file = "data/Ec_PES_32m.log2.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_25f.log2 <-
readr::read_csv(file = "data/Ec_PES_25f.log2.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.rank-tranformed
Ec_PES_32m.rank <-
readr::read_csv(file = "data/Ec_PES_32m.rank.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_25f.rank <-
readr::read_csv(file = "data/Ec_PES_25f.rank.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.rlog-tranformed
Ec_PES_32m.rlog <-
readr::read_csv(file = "data/Ec_PES_32m.rlog.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_25f.rlog <-
readr::read_csv(file = "data/Ec_PES_25f.rlog.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.PlotCategoryExpr()
Ectocaprus Male and Female (separately)
PlotCategoryExpr(
ExpressionSet = Ec_PES_32m.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Ectocarpus (male)",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10
## stage-centered "***" "***" "***" "***" "***" "***" "***" "***" "***" "***"
## PS11
## stage-centered "***"
PlotCategoryExpr(
ExpressionSet = Ec_PES_25f.log2,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Ectocarpus (female)",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10
## stage-centered "***" "***" "***" "***" "***" "***" "***" "***" "***" "***"
## PS11
## stage-centered "***"
PlotCategoryExpr(
ExpressionSet = Ec_PES_32m.rlog,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Ectocarpus (male)",
subtitle = "rlog(TPM)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10
## stage-centered "***" "***" "***" "***" "***" "***" "***" "***" "***" "***"
## PS11
## stage-centered "***"
PlotCategoryExpr(
ExpressionSet = Ec_PES_25f.rlog,
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Ectocarpus (female)",
subtitle = "rlog(TPM)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10
## stage-centered "***" "***" "***" "***" "***" "***" "***" "***" "***" "***"
## PS11
## stage-centered "***"
PlotCategoryExpr(
ExpressionSet = dplyr::select(Ec_PES_32m.log2, c(1,2) | dplyr::ends_with("PSP")),
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Ectocarpus (male) multicellular PSP",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10
## stage-centered "***" "***" "**" "***" "n.s." "***" "***" "***" "***" "***"
## PS11
## stage-centered "***"
PlotCategoryExpr(
ExpressionSet = dplyr::select(Ec_PES_25f.log2, c(1,2) | dplyr::ends_with("PSP")),
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Ectocarpus (female) multicellular PSP",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10 PS11
## stage-centered "***" "***" "*" "***" "**" "***" "***" "***" "***" "*" "*"
PlotCategoryExpr(
ExpressionSet = dplyr::select(Ec_PES_32m.log2, c(1,2) | dplyr::ends_with("GA")),
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Ectocarpus (male) multicellular GA",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10 PS11
## stage-centered "***" "***" "n.s." "***" "n.s." "*" "***" "***" "**" "**" "***"
PlotCategoryExpr(
ExpressionSet = dplyr::select(Ec_PES_25f.log2, c(1,2) | dplyr::ends_with("GA")),
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE) +
ggplot2::labs(
title = "Ectocarpus (female) multicellular GA",
subtitle = "log2(TPM+1)"
)## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10 PS11
## stage-centered "***" "***" "n.s." "***" "*" "***" "***" "***" "*" "**" "n.s."
Ectocaprus males vs females
Ec_PES_combined.log2 <-
dplyr::full_join(
Ec_PES_32m.log2 %>% dplyr::rename_at(vars(-1,-2), ~ paste0(., '_32m')),
Ec_PES_25f.log2 %>% dplyr::rename_at(vars(-1,-2), ~ paste0(., '_25f'))
)## Joining with `by = join_by(PS, GeneID)`Ec_PES_combined.rlog <-
dplyr::full_join(
Ec_PES_32m.rlog %>% dplyr::rename_at(vars(-1,-2), ~ paste0(., '_32m')),
Ec_PES_25f.rlog %>% dplyr::rename_at(vars(-1,-2), ~ paste0(., '_25f'))
)## Joining with `by = join_by(PS, GeneID)`To avoid repeating many lines, I wrote a function to make the plots easier when comparing the sexes.
PlotCategoryExpr_sexes <-
function(ExpressionSet, prefix, transformation){
OUT <-
PlotCategoryExpr(
ExpressionSet = dplyr::select(ExpressionSet, c(1,2) | dplyr::starts_with(prefix)),
legendName = "PS",
test.stat = TRUE,
type = "stage-centered",
distr.type = "boxplot",
log.expr = FALSE
) +
ggplot2::labs(
title = paste0("Ectocarpus (between sexes) in ", prefix),
subtitle = transformation
)
return(OUT)
}PlotCategoryExpr_sexes(Ec_PES_combined.log2, "meiospore", "log2(TPM+1)")## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10
## stage-centered "***" "***" "n.s." "*" "n.s." "*" "***" "n.s." "n.s." "n.s."
## PS11
## stage-centered "**"
PlotCategoryExpr_sexes(Ec_PES_combined.log2, "immGA", "log2(TPM+1)")## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10
## stage-centered "***" "***" "n.s." "n.s." "*" "n.s." "***" "n.s." "n.s." "n.s."
## PS11
## stage-centered "***"
PlotCategoryExpr_sexes(Ec_PES_combined.log2, "matGA", "log2(TPM+1)")## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10 PS11
## stage-centered "***" "***" "*" "***" "*" "***" "***" "***" "**" "*" "n.s."
PlotCategoryExpr_sexes(Ec_PES_combined.log2, "oldGA", "log2(TPM+1)")## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9
## stage-centered "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s."
## PS10 PS11
## stage-centered "n.s." "n.s."
PlotCategoryExpr_sexes(Ec_PES_combined.log2, "gamete", "log2(TPM+1)")## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9 PS10 PS11
## stage-centered "***" "***" "n.s." "*" "n.s." "n.s." "**" "*" "n.s." "n.s." "*"
PlotCategoryExpr_sexes(Ec_PES_combined.log2, "earlyPSP", "log2(TPM+1)")## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9
## stage-centered "***" "*" "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s."
## PS10 PS11
## stage-centered "n.s." "*"
PlotCategoryExpr_sexes(Ec_PES_combined.log2, "immPSP", "log2(TPM+1)")## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9
## stage-centered "***" "**" "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s."
## PS10 PS11
## stage-centered "n.s." "***"
PlotCategoryExpr_sexes(Ec_PES_combined.log2, "matPSP", "log2(TPM+1)")## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9
## stage-centered "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s."
## PS10 PS11
## stage-centered "n.s." "n.s."
PlotCategoryExpr_sexes(Ec_PES_combined.log2, "mitospore", "log2(TPM+1)")## PS1 PS2 PS3 PS4 PS5 PS6 PS7 PS8 PS9
## stage-centered "***" "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s." "n.s."
## PS10 PS11
## stage-centered "n.s." "n.s."
# PlotCategoryExpr_sexes(Ec_PES_combined.rlog, "meiospore", "rlog(TPM)")
# PlotCategoryExpr_sexes(Ec_PES_combined.rlog, "immGA", "rlog(TPM)")
# PlotCategoryExpr_sexes(Ec_PES_combined.rlog, "matGA", "rlog(TPM)")
# PlotCategoryExpr_sexes(Ec_PES_combined.rlog, "oldGA", "rlog(TPM)")
# PlotCategoryExpr_sexes(Ec_PES_combined.rlog, "gamete", "rlog(TPM)")
# PlotCategoryExpr_sexes(Ec_PES_combined.rlog, "earlyPSP", "rlog(TPM)")
# PlotCategoryExpr_sexes(Ec_PES_combined.rlog, "immPSP", "rlog(TPM)")
# PlotCategoryExpr_sexes(Ec_PES_combined.rlog, "matPSP", "rlog(TPM)")
# PlotCategoryExpr_sexes(Ec_PES_combined.rlog, "mitospore", "rlog(TPM)")Summary
In Fucus, PS1 and PS8 are clearly differentially expressed
on average between developmental stages (as defined in myTAI’s
PlotCategoryExpr()). PS2 and PS7 also give some traces of
differential expression. In F. distichus gametes, males have a
lower expression level across most PS’s (significant in PS1, PS2, PS6,
PS7 and PS8). In F. serratus gametes, this is also the case but
the differences are more pronounced (significant across all PS’s).
F. serratus is dioecious and in the matSP, we find that only
PS8 is significantly differentially regulated between sexes when
averaging all constituent tissues together.
Between tissue types of matSP in both Fucus species, essentially all gene age categories are differentially expressed. Focusing on the differences between sexes of matSP tissue types in the dioecious F. serratus, we see no significant differences between sexes in the vegetative tip (vegtip). PS8 is differentially expressed in the holdfast. PS1, PS2, PS7 and PS8 are differentially expressed in the stipe. As we would suspect, the sex-differences are greatest in the reproductive tip (reptip).
Together, this indicates that PS1 and PS8 genes are recruited to play a large role in the development of Fucus. PS8 plays a role in adult sex differentiation in dioecious F. serratus. In both F. serratus and F. distichus, the gametes differentially expresses many PS’s. Furthermore, it is surprising that most PS’s are differentially expressed between tissues, while the number of differentially expressed PS’s is more restricted during development. It is also surprising that many PS are differentially expressed between sexes in the stipe in the dioecious F. serratus, compared to other non-reproductive tissues such as the holdfast and vegtip. As we expect, differential PS expression is greatest between sexes in the reptip, whose cell-type composition foretells the differences in the gamete.
Compared to the age profile in Fucus, in Ectocarpus all gene ages are differentially expressed between developmental stages, even when considering just the multicellular PSP or GA. This points to a more dynamic transcriptional landscape through development, which involves genes from all levels of sequence-space conservation. When including all stages together, we see that the unicellar stages have less expression across all PS, though this difference could be due to the lower RNA quality in meiospores and mitospores. Lastly, unlike Ectocarpus, in Fucus, we lack some PS’s and most genes belong to PS1 and PS8. Thus, the resolution for this analysis is less fine in Fucus.
Get session info.
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.2 (2022-10-31)
## os macOS Big Sur ... 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Europe/Berlin
## date 2023-11-22
## pandoc 3.1.6.2 @ /usr/local/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## bit 4.0.5 2022-11-15 [1] CRAN (R 4.2.0)
## bit64 4.0.5 2020-08-30 [1] CRAN (R 4.2.0)
## bslib 0.5.1 2023-08-11 [1] CRAN (R 4.2.0)
## cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.0)
## callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.0)
## cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0)
## codetools 0.2-19 2023-02-01 [1] CRAN (R 4.2.0)
## colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0)
## crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.0)
## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.0)
## digest 0.6.33 2023-07-07 [1] CRAN (R 4.2.0)
## dplyr * 1.1.3 2023-09-03 [1] CRAN (R 4.2.0)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
## evaluate 0.22 2023-09-29 [1] CRAN (R 4.2.2)
## fansi 1.0.5 2023-10-08 [1] CRAN (R 4.2.2)
## farver 2.1.1 2022-07-06 [1] CRAN (R 4.2.0)
## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0)
## forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.2.0)
## foreach 1.5.2 2022-02-02 [1] CRAN (R 4.2.0)
## fs 1.6.3 2023-07-20 [1] CRAN (R 4.2.0)
## generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0)
## ggplot2 * 3.4.4 2023-10-12 [1] CRAN (R 4.2.2)
## glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
## gtable 0.3.4 2023-08-21 [1] CRAN (R 4.2.0)
## hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.0)
## htmltools 0.5.6.1 2023-10-06 [1] CRAN (R 4.2.2)
## htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.2.0)
## httpuv 1.6.11 2023-05-11 [1] CRAN (R 4.2.2)
## iterators 1.0.14 2022-02-05 [1] CRAN (R 4.2.0)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.0)
## jsonlite 1.8.7 2023-06-29 [1] CRAN (R 4.2.0)
## knitr 1.44 2023-09-11 [1] CRAN (R 4.2.0)
## labeling 0.4.3 2023-08-29 [1] CRAN (R 4.2.0)
## later 1.3.1 2023-05-02 [1] CRAN (R 4.2.2)
## lattice 0.21-9 2023-10-01 [1] CRAN (R 4.2.2)
## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0)
## lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.2.0)
## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
## Matrix 1.5-4.1 2023-05-18 [1] CRAN (R 4.2.0)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
## mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0)
## myTAI * 1.0.1.9000 2023-10-03 [1] Github (drostlab/myTAI@e159136)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0)
## pkgbuild 1.4.2 2023-06-26 [1] CRAN (R 4.2.0)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
## pkgload 1.3.3 2023-09-22 [1] CRAN (R 4.2.0)
## plyr 1.8.9 2023-10-02 [1] CRAN (R 4.2.2)
## prettyunits 1.2.0 2023-09-24 [1] CRAN (R 4.2.0)
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## profvis 0.3.8 2023-05-02 [1] CRAN (R 4.2.0)
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## ps 1.7.5 2023-04-18 [1] CRAN (R 4.2.0)
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## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
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## rmarkdown 2.25 2023-09-18 [1] CRAN (R 4.2.2)
## rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.2.0)
## sass 0.4.7 2023-07-15 [1] CRAN (R 4.2.0)
## scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
## shiny 1.7.5.1 2023-10-14 [1] CRAN (R 4.2.2)
## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
## stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
## tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.0)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.2.0)
## timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.0)
## tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.2.2)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis 2.2.2 2023-07-06 [1] CRAN (R 4.2.0)
## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
## vctrs 0.6.4 2023-10-12 [1] CRAN (R 4.2.2)
## viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.2.0)
## vroom 1.6.4 2023-10-02 [1] CRAN (R 4.2.2)
## withr 2.5.1 2023-09-26 [1] CRAN (R 4.2.0)
## xfun 0.40 2023-08-09 [1] CRAN (R 4.2.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
##
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